

"""simple docstring"""




import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
__UpperCAmelCase			=					logging.getLogger(__name__)


__UpperCAmelCase			=					'Hello world! cécé herlolip'


__UpperCAmelCase			=					namedtuple(
    'BertAbsConfig',
    [
        'temp_dir',
        'large',
        'use_bert_emb',
        'finetune_bert',
        'encoder',
        'share_emb',
        'max_pos',
        'enc_layers',
        'enc_hidden_size',
        'enc_heads',
        'enc_ff_size',
        'enc_dropout',
        'dec_layers',
        'dec_hidden_size',
        'dec_heads',
        'dec_ff_size',
        'dec_dropout',
    ],
)
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	List[Any]        =       BertAbsConfig(
     temp_dir="""."""				,     finetune_bert=__UpperCamelCase				,     large=__UpperCamelCase				,     share_emb=__UpperCamelCase				,     use_bert_emb=__UpperCamelCase				,     encoder="""bert"""				,     max_pos=512				,     enc_layers=6				,     enc_hidden_size=512				,     enc_heads=8				,     enc_ff_size=512				,     enc_dropout=0.2				,     dec_layers=6				,     dec_hidden_size=768				,     dec_heads=8				,     dec_ff_size=2048				,     dec_dropout=0.2				,     )
 UpperCAmelCase__       :	Dict        =       torch.load(__UpperCamelCase				,     lambda __UpperCamelCase				,     __UpperCamelCase   : storage   )
 UpperCAmelCase__       :	int        =       AbsSummarizer(__UpperCamelCase				,     torch.device("""cpu"""   )				,     __UpperCamelCase   )
 original.eval()

 UpperCAmelCase__       :	int        =       BertAbsSummarizer(__UpperCamelCase				,     torch.device("""cpu"""   )   )
 new_model.eval()

 # -------------------
 # Convert the weights
 # -------------------

 logging.info("""convert the model"""   )
 new_model.bert.load_state_dict(original.bert.state_dict()   )
 new_model.decoder.load_state_dict(original.decoder.state_dict()   )
 new_model.generator.load_state_dict(original.generator.state_dict()   )

 # ----------------------------------
 # Make sure the outpus are identical
 # ----------------------------------

 logging.info("""Make sure that the models' outputs are identical"""   )
 UpperCAmelCase__       :	List[Any]        =       BertTokenizer.from_pretrained("""bert-base-uncased"""   )

 # prepare the model inputs
 UpperCAmelCase__       :	List[Any]        =       tokenizer.encode("""This is sample éàalj'-."""   )
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCamelCase   ))   )
 UpperCAmelCase__       :	List[Any]        =       torch.tensor(__UpperCamelCase   ).unsqueeze(0   )
 UpperCAmelCase__       :	Any        =       tokenizer.encode("""This is sample 3 éàalj'-."""   )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__UpperCamelCase   ))   )
 UpperCAmelCase__       :	List[str]        =       torch.tensor(__UpperCamelCase   ).unsqueeze(0   )

 # failsafe to make sure the weights reset does not affect the
 # loaded weights.
 assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight   )   ) == 0

 # forward pass
 UpperCAmelCase__       :	List[Any]        =       encoder_input_ids
 UpperCAmelCase__       :	Optional[Any]        =       decoder_input_ids
 UpperCAmelCase__       :	str        =       None
 UpperCAmelCase__       :	Optional[int]        =       None
 UpperCAmelCase__       :	List[str]        =       None
 UpperCAmelCase__       :	Tuple        =       None
 UpperCAmelCase__       :	int        =       None

 # The original model does not apply the geneator layer immediatly but rather in
 # the beam search (where it combines softmax + linear layer). Since we already
 # apply the softmax in our generation process we only apply the linear layer here.
 # We make sure that the outputs of the full stack are identical
 UpperCAmelCase__       :	Tuple        =       original(__UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )[0]
 UpperCAmelCase__       :	Dict        =       original.generator(__UpperCamelCase   )

 UpperCAmelCase__       :	List[Any]        =       new_model(
     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   )[0]
 UpperCAmelCase__       :	Any        =       new_model.generator(__UpperCamelCase   )

 UpperCAmelCase__       :	int        =       torch.max(torch.abs(output_converted_model - output_original_model   )   ).item()
 print("""Maximum absolute difference beween weights: {:.2f}""".format(__UpperCamelCase   )   )
 UpperCAmelCase__       :	Union[str, Any]        =       torch.max(torch.abs(output_converted_generator - output_original_generator   )   ).item()
 print("""Maximum absolute difference beween weights: {:.2f}""".format(__UpperCamelCase   )   )

 UpperCAmelCase__       :	Union[str, Any]        =       torch.allclose(__UpperCamelCase				,     __UpperCamelCase				,     atol=1e-3   )
 if are_identical:
  logging.info("""all weights are equal up to 1e-3"""   )
 else:
  raise ValueError("""the weights are different. The new model is likely different from the original one."""   )

 # The model has been saved with torch.save(model) and this is bound to the exact
 # directory structure. We save the state_dict instead.
 logging.info("""saving the model's state dictionary"""   )
 torch.save(
     new_model.state_dict()				,     """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""   )


if __name__ == "__main__":
      __UpperCAmelCase			=					argparse.ArgumentParser()
      parser.add_argument(
          '--bertabs_checkpoint_path',
          default=None,
          type=str,
          required=True,
          help='Path the official PyTorch dump.',
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default=None,
          type=str,
          required=True,
          help='Path to the output PyTorch model.',
      )
      __UpperCAmelCase			=					parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )








from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
		import timm


if is_torch_available():
		from torch import Tensor







class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	'''pixel_values'''
							UpperCAmelCase_						:		List[str]								=	False
							UpperCAmelCase_						:		Tuple								=	TimmBackboneConfig




							def __init__(					self    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       int:
														requires_backends(self    ,						'''timm'''						)
														super().__init__(__UpperCAmelCase						)
														A      :					List[str]      		=					config

														if config.backbone is None:
																					raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''						)

														if config.backbone not in timm.list_models():
																					raise ValueError(f'backbone {config.backbone} is not supported by timm.'						)

														if hasattr(__UpperCAmelCase    ,						'''out_features'''						) and config.out_features is not None:
																					raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''						)

														A      :					str      		=					getattr(__UpperCAmelCase    ,						'''use_pretrained_backbone'''    ,						__UpperCAmelCase						)
														if pretrained is None:
																					raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''						)

														# We just take the final layer by default. This matches the default for the transformers models.
														A      :					Optional[int]      		=					config.out_indices if getattr(__UpperCAmelCase    ,						'''out_indices'''    ,						__UpperCAmelCase						) is not None else (-1,)

														A      :					str      		=					timm.create_model(
														    config.backbone    ,						pretrained=__UpperCAmelCase    ,						features_only=config.features_only    ,						in_chans=config.num_channels    ,						out_indices=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														A      :					str      		=					self._backbone.return_layers
														A      :					Any      		=					{layer['''module''']: str(__UpperCAmelCase						) for i, layer in enumerate(self._backbone.feature_info.info						)}
														super()._init_backbone(__UpperCAmelCase						)




							@classmethod
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Any:
														requires_backends(cls    ,						['''vision''', '''timm''']						)
														from ...models.timm_backbone import TimmBackboneConfig

														A      :					Optional[Any]      		=					kwargs.pop('''config'''    ,						TimmBackboneConfig()						)

														A      :					str      		=					kwargs.pop('''use_timm_backbone'''    ,						__UpperCAmelCase						)
														if not use_timm:
																					raise ValueError('''use_timm_backbone must be True for timm backbones'''						)

														A      :					Optional[int]      		=					kwargs.pop('''num_channels'''    ,						config.num_channels						)
														A      :					List[str]      		=					kwargs.pop('''features_only'''    ,						config.features_only						)
														A      :					Optional[int]      		=					kwargs.pop('''use_pretrained_backbone'''    ,						config.use_pretrained_backbone						)
														A      :					Optional[int]      		=					kwargs.pop('''out_indices'''    ,						config.out_indices						)
														A      :					int      		=					TimmBackboneConfig(
														    backbone=__UpperCAmelCase    ,						num_channels=__UpperCAmelCase    ,						features_only=__UpperCAmelCase    ,						use_pretrained_backbone=__UpperCAmelCase    ,						out_indices=__UpperCAmelCase    ,						)
														return super()._from_config(__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       Any:
														pass






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase						)		->       Union[BackboneOutput, Tuple[Tensor, ...]]:
														A      :					Optional[int]      		=					return_dict if return_dict is not None else self.config.use_return_dict
														A      :					str      		=					(
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														A      :					Any      		=					output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError('''Cannot output attentions for timm backbones at the moment'''						)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					A      :					Dict      		=					self._all_layers
																					A      :					int      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					Any      		=					self._return_layers
																					A      :					Union[str, Any]      		=					tuple(hidden_states[i] for i in self.out_indices						)
														else:
																					A      :					Optional[int]      		=					self._backbone(__UpperCAmelCase    ,						**__UpperCAmelCase						)
																					A      :					List[Any]      		=					None

														A      :					str      		=					tuple(__UpperCAmelCase						)
														A      :					int      		=					tuple(__UpperCAmelCase						) if hidden_states is not None else None

														if not return_dict:
																					A      :					Optional[Any]      		=					(feature_maps,)
																					if output_hidden_states:
																												A      :					Tuple      		=					output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__UpperCAmelCase    ,						hidden_states=__UpperCAmelCase    ,						attentions=__UpperCAmelCase						)






